Seeing this tweet from Guido van Rossum the other day prompted me to write this “OMG, Python 2 is going away SOON” article. You have definitely heard it before, but seriously, folks, the Python upstream community is ending support for Python 2 at the end of the year!
Let’s stop saying “2020” because that sounds far away when, in fact, we are talking about January 1, 2020, which is two and half months from now. In this article, I’ll provide some quick links and basic information to help you make the move to Python 3.
Continue reading “Python 2 support is going away soon: Make the move to Python 3”
We are excited to announce a new release of Red Hat Dependency Analytics, a solution that enables developers to create better applications by evaluating and adding high-quality open source components, directly from their IDE.
Red Hat Dependency Analytics helps your development team avoid security and licensing issues when building your applications. It plugs into the developer’s IDE, automatically analyzes your software composition, and provides recommendations to address security holes and licensing problems that your team may be missing.
Without further ado, let’s jump into the new capabilities offered in this release. This release includes a new version of the IDE plugin and the server-side analysis service hosted by Red Hat.
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Python has become a popular programming language in the AI/ML world. Projects like TensorFlow and PyTorch have Python bindings as the primary interface used by data scientists to write machine learning code. However, distributing AI/ML-related Python packages and ensuring application binary interface (ABI) compatibility between various Python packages and system libraries presents a unique set of challenges.
The manylinux standard (e.g., manylinux2014) for Python wheels provides a practical solution to these challenges, but it also introduces new challenges that the Python community and developers need to consider. Before we delve into these additional challenges, we’ll briefly look at the Python ecosystem for packaging and distribution.
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In my previous article, Run Red Hat Enterprise Linux 8 in a container on RHEL 7, I showed how to start developing with the latest versions of languages, databases, and web servers available with Red Hat Enterprise Linux 8 even if you are still running RHEL 7. In this article, I’ll build on that base to show how to get started with the Flask microframework using the current RHEL 8 application stream version of Python 3.
From my perspective, using Red Hat Enterprise Linux 8 application streams in containers is preferable to using software collections on RHEL 7. While you need to get comfortable with containers, all of the software installs in the locations you’d expect. There is no need to use
scl commands to manage the selected software versions. Instead, each container gets an isolated user space. You don’t have to worry about conflicting versions.
Continue reading “Develop with Flask and Python 3 in a container on Red Hat Enterprise Linux”
This article explains how to configure a Python application running within an OpenShift pod to communicate with the Red Hat OpenShift cluster via
openshift-restclient-python, the OpenShift Python client.
Continue reading “Controlling Red Hat OpenShift from an OpenShift pod”
The Coderland booth at the recent Red Hat Summit was all about serverless computing as implemented in the Compile Driver. If you haven’t gone through that example (you really should), that code creates a souvenir photo by superimposing the Coderland logo, a date stamp, and a message on top of an image from a webcam. We thought it would be fun to build a Raspberry Pi version for the booth so we could offer attendees a free souvenir. Here’s a look at the finished product:
Continue reading “How to build a Raspberry Pi photo booth”
Red Hat OpenShift is part of the Cloud Native Computing Foundation (CNCF) Certified Program, ensuring portability and interoperability for your container workloads. This also allows you to use Kubernetes tools to interact with an OpenShift cluster, like
kubectl, and you can rest assured that all the APIs you know and love are right there at your fingertips.
The Kubernetes Python client is another great tool for interacting with an OpenShift cluster, allowing you to perform actions on Kubernetes resources with Python code. It also has applications within a cluster. We can configure a Python application running on OpenShift to consume the OpenShift API, and list and create resources. We could then create containerized batch jobs from the running application, or a custom service monitor, for example. It sounds a bit like “OpenShift inception,” using the OpenShift API from services created using the OpenShift API.
In this article, we’ll create a Flask application running on OpenShift. This application will use the Kubernetes Python client to interact with the OpenShift API, list other pods in the project, and display them back to the user.
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Red Hat CodeReady Workspaces provide developers with containerized development environments hosted on OpenShift/Kubernetes. DevOps teams can now use a hosted development environment that’s pre-built for their chosen stack and customized for their project.
CodeReady Workspaces can help you rapidly onboard developers for your project as everything they need to develop is running in a containerized workspace. In this post, we’re going to use CodeReady Workspaces to get up and running quickly with an existing open source project, Peak. Peak is a multi-container Kubernetes application for performance testing web services, and it allows you to create distributed performance tests using the Kubernetes Batch API for test orchestration. We’ll make some modifications to Peak’s Flask front end, a stateless web interface that interacts with a Falcon RESTful API to return data about performance tests. You won’t need the complete Peak application deployed, though if you like, you can find steps to deploy it to OpenShift here.
To follow along you’ll need a Red Hat OpenShift Container Platform 3.11 environment. You can use the Red Hat Container Development Kit on your Windows, macOS, or Linux laptop or a hosted Red Hat OpenShift instance to do it on online.
Continue reading “Creating a containerized Python/Flask development environment with Red Hat CodeReady Workspaces”
TL;DR Of course we have Python! You just need to specify if you want Python 3 or 2 as we didn’t want to set a default. Give
yum install python3 and/or
yum install python2 a try. Or, if you want to see what we recommend you install
yum install @python36 or
yum install @python27. Read on for why:
For prior versions of Red Hat Enterprise Linux, and most Linux Distributions, users have been locked to the system version of Python unless they got away from the system’s package manager. While this can be true for a lot of tools (ruby, node, Perl, php) the Python use case is more complicated because so many Linux tools (like yum) rely on Python. In order to improve the experience for RHEL 8 users, we have moved the Python used by the system “off to the side” and we introduced the concept of Application Streams based on Modularity.
Continue reading “What, No Python in RHEL 8 Beta?”
Ten years ago, the developers of the Python programming language decided to clean things up and release a backwards-incompatible version, Python 3. They initially underestimated the impact of the changes, and the popularity of the language. Still, in the last decade, the vast majority of community projects has migrated to the new version, and major projects are now dropping support for Python 2.
In Red Hat Enterprise Linux 8, Python 3.6 is the default. But Python 2 remains available in RHEL 8.
Using Python in RHEL 8
To install Python, type
yum install python3.
To run Python, type
If that doesn’t work for you, or you need more details, read on!
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